Multispectral imaging of nailfold capillaries using light-emitting diode illumination

Abstract. Significance The capillaries are the smallest blood vessels in the body, typically imaged using video capillaroscopy to aid diagnosis of connective tissue diseases, such as systemic sclerosis. Video capillaroscopy allows visualization of morphological changes in the nailfold capillaries but does not provide any physiological information about the blood contained within the capillary network. Extracting parameters such as hemoglobin oxygenation could increase sensitivity for diagnosis and measurement of microvascular disease progression. Aim To design, construct, and test a low-cost multispectral imaging (MSI) system using light-emitting diode (LED) illumination to assess relative hemoglobin oxygenation in the nailfold capillaries. Approach An LED ring light was first designed and modeled. The ring light was fabricated using four commercially available LED colors and a custom-designed printed circuit board. The experimental system was characterized and results compared with the illumination model. A blood phantom with variable oxygenation was used to determine the feasibility of using the illumination-based MSI system for oximetry. Nailfold capillaries were then imaged in a healthy subject. Results The illumination modeling results were in close agreement with the constructed system. Imaging of the blood phantom demonstrated sensitivity to changing hemoglobin oxygenation, which was in line with the spectral modeling of reflection. The morphological properties of the volunteer capillaries were comparable to those measured in current gold standard systems. Conclusions LED-based illumination could be used as a low-cost approach to enable MSI of the nailfold capillaries to provide insight into the oxygenation of the blood contained within the capillary network.

voltage was applied, and simultaneously, logging of the irradiance at the photodiode sensor commenced. The irradiance was recorded at a sample rate of 1 sample per second for a period of 30 minutes. The experiment was repeated for the cyan and amber LEDs separately and a plot was drawn to compare the variation of irradiance over time ( Supplementary Fig. 2).

Spectral Characterization
The spectral profile of the LEDs and the linearity of the sensors used in the experimental systems were confirmed using the experimental system illustrated in Supplementary Fig. 3. The relative irradiance of each LED type was measured using a spectrometer (Avantes AvaSpec-ULS2048-USB2-FCPC), which was USB-connected to a computer running the manufacturer's software (AvaSoft v8). The spectrometer was first calibrated against the reference spectral profile of a halogen light source (Avantes AvaLight-HAL-CAL-MINI), which was connected to the spectrometer using a multimode fiber patch cable with 600 µm core (Thorlabs M134-L02).
Following calibration, the patch cable was used to connect the spectrometer to the output port of sphere and driven at a nominal constant current recommended in its datasheet. A silicon photodiode (Thorlabs SM05PD1B) at a second output port was used to determine when the LED peak output had stabilized following warm-up. The photodiode output was converted to a measurable voltage using a transimpedance amplifier (Thorlabs AMP100). The spectral profile of the LED was stored as a .csv data file and plotted using MATLAB software ( Supplementary Fig.   4). The spectral properties of the cyan, green, and amber LEDs are summarized in Supplementary   Table 1. The measured spectral properties were similar to those of the datasheet for the LEDs. The only one that showed a slight difference was the amber since the peak is slightly off (6 nm) but still within the tolerance given on the datasheet.  The output port of the sphere acted as a disk-shaped homogeneous illumination source of diameter 10.9 mm. The camera sensor (without lens) was positioned coaxially with this port at a distance of 108 mm from the sphere's opening. Basler Pylon Viewer v6.2 was used to set the camera parameters and capture the raw images. A signal offset of 24 DN was added to the camera signal span of 0 to 4095 DN to stop the noise level at zero irradiance from clipping the lower boundary of the measurement range.

Supplementary
It was assumed that, for a fixed irradiance, the sensor's mean digital output signal value, μy, was linearly proportional to the number of incident photons per pixel, μp: 33 where μy.dark is the mean signal value with no light incident on the sensor, K is the system gain, and η is total quantum efficiency. It was also assumed that the variance of the output noise, σy², was proportional to the mean output signal multiplied by the gain: 33 σy² -σy.dark² = K(μy -μy.dark), where σy.dark² is the variance of the output noise with no light on the sensor. The histogram of the pixels' greyscale values was examined in real-time, and the camera exposure time increased until >99% of the pixels were below saturation. Two sequential greyscale images were captured at this exposure time. The LED light source was then turned off, and two dark reference images were captured at the same exposure time. The exposure time was decreased in steps of 0.5 ms and the two greyscale and two dark image capture process repeated at each step. The experiment was conducted at constant room temperature. The system gain of the camera was determined to be 0.529 from the slope of this photon transfer curve ( Supplementary Fig. 5).
The linearity of the Grasshopper sensor (FLIR Grasshopper Camera GS3-U3-41C6M-C) was checked using the same set-up as Supplementary Fig. 3, but a white LED (Dolan-Jenner MI-LED-UK-A7) was coupled into the integrating sphere using a fiber optic, instead of the individual LEDs. The intensity of the light from the LED was varied at 13 different intensities, validated with the reference spectrometer. The area under the curve of the spectrometer data was taken at each illumination point and results were normalized to the highest illumination intensity. The sensor response was evaluated using the SpinView v2.2.0.48 software package (Teledyne FLIR) to find the mean greyscale value. The process was repeated ten times to find the average sensor response and illumination intensity ( Supplementary Fig. 6), which was demonstrated to be linear with an R2= 0.9949.

Supplementary Fig. 6: Sensor characterization (FLIR Grasshopper) using a white LED source. (a)
The sensor was illuminated uniformly by LED light, and the signal mean and variance were calculated for two images at each illumination intensity.

LED Modeling of Cyan with different viewing angles and angles of incidence
The cyan LED system model was adjusted to better understand the difference in illumination profiles of the system. This was done two ways: the viewing angle was decreased to half of the initial model (from 15° to 7.5°); and the angle of incidence, or angle made between the LEDs and the z axis, was decreased by 10° (from 32.3° to 22.3°). The resulting profiles are shown in Supplementary Fig. 7 and the RMSE in the Z and X planes are summarized in Supplementary  Sensor response normalized continued discrepancy could be due to misalignment of the measuring systems or asymmetries of LED placement in the experimental system. The same trend could be seen in the RMSE of the x axis, which indicates the decreased angle of incidence better models the built system than the original and remodel, with a smaller viewing angle.  Fig. 7: The (a) measured cyan system's drop off along the z axis was compared to the (b) initial model and two separate remodels that varied the (c) viewing angle and (d) angle of incidence. (e) The associated profiles are shown to illustrate this comparison. The same was done in the x axis (f-j).

Image reconstruction of the nailfold capillaries
Two and three wavelength reconstruction of the relative oxygenation in the nailfold capillaries was explored by calculating three different images using the narrowband images collected, after image processing performed as described in Section 2.7. Three different images were calculated by the following equations: The resulting images are shown in Supplementary Fig. 8. For each of these images the SNR, signal, background signal, and background noise were calculated (Table 3). The image calculated using Eqn. 3 had the highest SNR compared to the other two methods, in part due to the lower noise.